The use of higher order statistics for noise suppression and estimation is well known. With higher order statistics it has been possible to derive more information from a received signal than with second order statistics which have commonly been used in telecommunications. For example, the phase of the transmission channel may be derived from the stationary received signal using higher order statistics. Another benefit of higher order statistic noise suppression is the suppression of Gaussian noise.
One such higher order statistic noise suppression method is disclosed by Steven F. Boll in “Suppression of Acoustic Noise in Speech Using Spectral Subtraction”, IEEE Transactions on Acoustics, Speech, and Signal Processing, VOL. ASSP-27, No. 2, April 1979. This spectral subtraction method comprises the systematic computation of the average spectra of a signal and a noise in some time interval and afterwards through the subtraction of both spectral representations. Spectral subtraction assumes (i) a signal is contaminated by a broadband additive noise, (ii) a considered noise is locally stationary or slowly varying in short intervals of time, (iii) the expected value of a noise estimate during an analysis is equal to the value of the noise estimate during a noise reduction process, and (iv) the phase of a noisy, pre-processed and noise reduced, post-processed signal remains the same. Spectral subtraction and known higher order statistic noise suppression methods encounter difficulties when tracking a ramping noise source and do little to reduce the noise contamination in a ramping, severe or non-stationary acoustic noise environment.
For example, FIG. 1 illustrates speech of a male speaker (“Tom's birthday is in June”, “Frank's neighbor mowed his lawn”, “Clip the pens on the books”) in the presence of Gaussian acoustic noise. The illustrated Gaussian noise source contains an amplitude increased ramp at a one dB/second rate. Many noise cancellation algorithms have difficulty tracking a moving noise source of this type. A real world example of this condition is speech that is recorded in a stationary noise level environment such as in recording speech outdoors with a car passing at a distance to the recording device. The noise increases with a relatively constant level and then decreases back down to a fixed stationary level.
FIG. 2 illustrates speech of a female speaker (“Do not drink the Coke fast”, “Please rent the car to him”, “Invest your money now”) recorded in the presence of CH-47 helicopter noise. The helicopter noise source is characterized by non-stationary noise and loud volumes resulting in poor Signal-to-Noise-Ratio (SNR) conditions. Typically, digital voice systems are often completely unusable for communications in the presence of such non-stationary noise. Hence, there exists a need in the art for a system and method to improve the intelligibility and quality of speech in the presence of ramping, severe or non-stationary acoustic noise environments.
Therefore, it is an object of the disclosed subject matter to overcome these and other problems in the art and present a novel method and system for noise cancellation with noise ramp tracking in the presence of ramping, severe or non-stationary acoustic noise environments.
It is an object of the disclosed subject matter to present a novel method to reduce the noise source of an input speech signal in a telecommunications system using minimal computational complexity. It is a further object to estimate the noise level present in an input speech signal when the noise source is ramping up or down in amplitude (at least 2-3 dB/second), to correctly identify speech segments from noise only segments so that speech may not degrade when noise levels are varied in amplitude, and to automatically adapt the resulting post-processed noise level to a suitable level even when noise is not present in the input speech.
It is also an object of the disclosed subject matter to present a novel method to filter the noise source of an input speech signal by estimating the noise level present, modify the input speech signal based on the noise estimate, identify and separate speech segments from noise only segments, and adapt post-processed noise levels to an acceptable level.
It is a further object of the disclosed subject matter to present a novel method of noise cancellation by applying a windowed Fourier transform to an input speech signal, estimating the noise level present in an input speech signal, modifying the input speech signal based on the noise estimate, identifying speech segments from the noise only segments, and adapting post-processed noise levels to acceptable levels.
It is an object of the disclosed subject matter to present a novel system for noise cancellation in a severe acoustic environment comprising an input device operably connected to a processor, a processor operably connected to an electronic memory and storage device wherein the processor conducts a noise cancellation technique, a filter for adapting post-processed noise levels to acceptable levels, a storage device operably connected to the processor for storing and applying noise histograms for further noise processing, and an output device operably connected to the processor for communicating the output speech signal.
These and many other objects and advantages of the present invention will be readily apparent to one skilled in the art to which the invention pertains from a perusal of the claims, the appended drawings, and the following detailed description of the preferred embodiments.